Resume Shortlisting based on the Job description for Academic Institution using NLP

Manually shortlisting resumes is labor-intensive and inefficient, especially with large volumes, leading to inconsistencies. This research leverages NLP and Named Entity Recognition (NER) to extract key details like education, skills, and work experience from resumes, automating the shortlisting process. A system was developed to rank candidates based on job relevance, generating downloadable Excel files and providing insights through visuals. The proposed method significantly reduces manual effort and time, improving accuracy and efficiency in candidate selection.